CN110930577A - Method for analyzing unregistered but actually living in personnel based on entrance guard data - Google Patents
Method for analyzing unregistered but actually living in personnel based on entrance guard data Download PDFInfo
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Abstract
The invention relates to the technical field of face recognition and big data processing in intelligent entrance guards, in particular to a method for analyzing unregistered but actually-living people based on entrance guard data. The invention has the beneficial effects that: the display is performed from high to low according to the frequency of the unregistered personnel, so that the priority and the efficiency of the unregistered personnel to be treated are improved, and meanwhile, which buildings need to be treated as soon as possible can be accurately determined; in addition, takeout and express delivery personnel are filtered through machine deep learning, and partial interference data are eliminated.
Description
Technical Field
The invention relates to the technical field of face recognition and big data processing in intelligent access control, in particular to a method for analyzing unregistered but actually living people based on access control data.
Background
With the further development of AI biometric technology, biometric technologies such as human face and gait are becoming more and more mature. Various community information acquisition APPs, the wisdom community project is more and more. In the urban village floating population management, how to facilitate accurate management and control by science and technology better for people is the direction of active exploration and research in the current social management project.
The personnel information of living in is collected through modes such as cell-phone APP or autonomic application, this information and people's face picture information are advanced including personnel, adopt the people's face to open the door, multiple modes such as access card, key, fingerprint to carry out the management of cominging in and going out, generally through building card, people's face, fingerprint, password etc. bind the mode and carry out the management and control of cominging in and going out. Generally, in the management of rental houses, a rental house manager manually checks the information of mobile personnel to residents at regular time, and then manually reports the information in various rental house management systems.
However, in the practical application process, the prior art performs unregistered personnel control through various anti-following devices or unregistered and non-admission access restriction, and strict control is mostly applied to closed cells, and for the case of open urban villages with a large number of floating population, the modes of door control or gate machine are not very suitable. The workload of manually verifying the registration information of the floating population is huge, and information is easy to miss. The automatic declaration and other modes easily cause the inconsistency of the registered certificates of the actual check-in personnel.
At present, urban village floating population management is a difficult problem of urban grid management, in recent years, a plurality of innovative floating population management methods are also presented, and the problems that registered people are inconsistent with actual check-in situations and the like still exist in common modes of active reporting by landlord or autonomous Application (APP) reporting and the like. Depending on the verification of a grid operator or a worker at home, and because a tenant and the like are often not at home, omission is difficult to avoid under the conditions of hiding, misinformation and the like; for the conditions that many open urban villages, relatives and friends visit occasionally, move frequently, and rent and actual attendance are inconsistent, the dependence on a strict and complex access control management method brings great management cost and inconvenience, and the use method of high-tech or complex APP has the problems of training cost, inconvenience for the old and the like.
In view of the above, it is desirable to design a method for accurately analyzing unregistered but actually staying in for a long time through human face big data.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a method for analyzing unregistered but actually-living people based on entrance guard data.
In order to achieve the above object, the technical solution of the present invention is: a method of analyzing unregistered but actually living persons based on access control data, comprising: access control system, the sensor, mobile terminal APP, third party database, video collection station and the high in the clouds server that detect entrance guard's on-off state, include following step:
s1: and importing and loading the tenant and the householder information of the personnel who have already entered the house through the mobile terminal APP or the third-party database, wherein the tenant and the householder information of the personnel who have entered the house comprise: comparing the biological characteristics of the certificate photo, the life photo, the video data, the fingerprint and the like;
s2: the sensor for detecting the opening and closing state of the entrance guard detects that people enter and exit the building, and automatically triggers a video collector fixedly installed on an entrance guard system to collect video data or photo data within the time period of the people entering and exiting the building as long as door opening and closing operation occurs;
s3: the cloud server calls video data or photo data collected by the video collector in the time period, the video data or photo data collected by the video collector and the tenant and householder information of the people who live in S1 are compared and identified through a face recognition module of the cloud server, if the video data or photo data are matched, the video data or photo data are classified as matched data, the matched data are returned to S1 for archiving again, the video data or photo data are archived as registered people, and if the video data or photo data are not matched, the video data or the photo data are classified as unmatched data;
s4: marking the unmatched materials in the S3 as unregistered persons, and establishing unregistered person labels according to the unmatched materials; when the steps from S1 to S3 are circulated again, and the matching of the registered person fails, the registered person is matched with the unregistered person, and the video data or the photo data of the same person entering and exiting the access control system are classified under the label of the person according to the contrast similarity;
s5: and looping S1 to S4, accumulating the video data or the photo data of the unregistered personnel, and establishing an unregistered personnel tag library to obtain the space-time rules of all the unregistered personnel.
Furthermore, the face recognition module of the cloud server can extract attribute models of video data or photo data of unregistered personnel, train and recognize feature data such as work clothes, work caps and signs, and establish label classification storage.
Further, access control system with all be provided with the remote communication module on the high in the clouds server, the remote communication module includes: any one or more of a WIFI module, a 5G communication module, a 4G communication module, a 3G communication module and a 2G communication module.
Further, the cloud server is configured to process and integrate the three-dimensional face information and mapping information thereof obtained by importing and loading the video collector, the mobile terminal APP and a third-party database, and complete the step loop operations from S1 to S4.
Furthermore, the face recognition module is used for recognizing, analyzing and calculating face data collected by the video collector, synthesizing all information of each user entering and exiting the access control system through a face information iterative algorithm, and completing iterative learning along with continuous updating of the data.
Further, video collector fixed mounting is in on access control system's the access control machine, video collector with detect the sensor electric connection of entrance guard on-off state, work as when the access control machine is opened, the sensor starts video collector candids the personnel and gets into the access control system overall process, works as when the access control machine is closed, the sensor is closed video collector, video collector passes through remote communication module with high in the clouds server communication is connected.
Furthermore, the grid operator can enter the cloud server through the WEB interface, inquire the tag library of the unregistered personnel, inquire the data of the unregistered personnel in the grid range according to the frequency or the building, and further perform service processing.
The invention has the beneficial effects that:
the invention provides a method for analyzing unregistered but actually living people based on access control data, which utilizes the data of biological characteristics in a period of time to carry out statistical analysis on the data. The statistical analysis of the frequency of occurrence of unregistered persons provides a means to assist in the management of mobile personnel. The display is performed from high to low according to the frequency of the unregistered personnel, so that the priority and the efficiency of the unregistered personnel to be treated are improved, and meanwhile, which buildings need to be treated as soon as possible can be accurately determined; meanwhile, the model for recognizing the characteristics of the express and take-away personnel wearing is trained and trained through a machine, secondary filtering is carried out, personnel labels are classified, and partial interference data are eliminated.
The invention can also be used for studying and judging the conditions that actual personnel staying in is inconsistent with the registered certificate and the like, and is used for early warning key personnel in an actual security scene.
Drawings
Fig. 1 is a logic diagram of a method for analyzing unregistered but actually checked-in persons based on entrance guard data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments disclosed herein are intended to be within the scope of the present invention.
Referring to fig. 1, a method for analyzing unregistered but actually checked-in persons based on entrance guard data includes: access control system, the sensor, mobile terminal APP, third party database, video collection station and the high in the clouds server that detect entrance guard's on-off state, include following step:
s1: and importing and loading the tenant and the householder information of the personnel who have already entered the house through the mobile terminal APP or the third-party database, wherein the tenant and the householder information of the personnel who have entered the house comprise: comparing the biological characteristics of the certificate photo, the life photo, the video data, the fingerprint and the like;
s2: the sensor for detecting the opening and closing state of the entrance guard detects that people enter and exit the building, and automatically triggers a video collector fixedly installed on an entrance guard system to collect video data or photo data within the time period of the people entering and exiting the building as long as door opening and closing operation occurs;
s3: the cloud server calls video data or photo data collected by the video collector in the time period, the video data or photo data collected by the video collector and the tenant and householder information of the people who live in S1 are compared and identified through a face recognition module of the cloud server, if the video data or photo data are matched, the video data or photo data are classified as matched data, the matched data are returned to S1 for archiving again, the video data or photo data are archived as registered people, and if the video data or photo data are not matched, the video data or the photo data are classified as unmatched data;
s4: marking the unmatched materials in the S3 as unregistered persons, and establishing unregistered person labels according to the unmatched materials; when the steps from S1 to S3 are circulated again, and the matching of the registered person fails, the registered person is matched with the unregistered person, and the video data or the photo data of the same person entering and exiting the access control system are classified under the label of the person according to the contrast similarity;
s5: and looping S1 to S4, accumulating the video data or the photo data of the unregistered personnel, and establishing an unregistered personnel tag library to obtain the space-time rules of all the unregistered personnel.
In this embodiment, the face recognition module of the cloud server may perform attribute model extraction on video data or photo data of unregistered people, perform training recognition on feature data such as work clothes, work caps, signs, and the like, and establish tag classification storage.
In this embodiment, access control system with all be provided with the remote communication module on the high in the clouds server, the remote communication module includes: any one or more of a WIFI module, a 5G communication module, a 4G communication module, a 3G communication module and a 2G communication module.
In this embodiment, the cloud server is configured to process and integrate the three-dimensional face information and the mapping information thereof obtained by importing and loading the video collector, the mobile terminal APP, and the third-party database, and complete the step loop operations from S1 to S4.
In this embodiment, the face recognition module is configured to recognize, analyze, and calculate face data collected by the video collector, synthesize all information of each user who enters and exits the access control system through a face information iterative algorithm, and complete iterative learning with continuous update of the data.
In this embodiment, video collector fixed mounting is in on access control system's the access control machine, video collector with detect the sensor electric connection of entrance guard on-off state, work as when the access control machine is opened, the sensor starts video collector candids the personnel and gets into the access control system overall process, works as when the access control machine is closed, the sensor is closed video collector, video collector passes through remote communication module with high in the clouds server communication is connected.
In this embodiment, the grid operator may enter the cloud server through the WEB interface, query the tag library of unregistered personnel, query the data of the unregistered personnel in the grid range according to the frequency or the building, and then perform service processing.
Example 1
At first with installing sensor and the video collector that is used for detecting entrance guard's on-off state on the entrance guard machine of taxi room area, video collector and the sensor electric connection who detects entrance guard's on-off state, when the entrance guard machine was opened, the sensor starts video collector and takes a candid photograph personnel and get into the access control system overall process, and when the entrance guard machine was closed, the video collector was closed to the sensor, and video collector passes through remote communication module and is connected with high in the clouds server communication.
And uploading the existing registered personnel picture data to a cloud server, keeping the data synchronous, and ensuring the consistency of personnel information. Then, a plurality of pictures of various common take-away personnel or various common take-away pictures of express delivery personnel and a plurality of pictures of other non-express delivery take-away personnel are downloaded and collected on the network, and then the pictures are converted into an lmdb format after manual marking and input into a coffee deep learning framework for machine learning training. And obtaining a filtering model kdModelFilter of various express delivery personnel or takeout personnel.
Referring to the attached figure 1, firstly, the whole process that a video collector in an access control system collects and captures a person entering the access control system is carried out, a communication module in the access control system transmits video data or picture data, collected and captured by the video collector, entering the access control system to a cloud server, the server compares the data with a registered person information base through a face recognition module, if the comparison is successful, the collected video data or picture data is filed in a registered person label, and the collection capture time and place are recorded; if the comparison fails, comparing the collected video data or picture data with the unregistered personnel tag library, if the comparison succeeds, filing the video data or picture data into the unregistered personnel tag, and recording the acquisition snapshot time and place; and if the comparison of the unregistered personnel tag library fails, filtering the video data or the picture data acquired and captured at the time through a kdModelFilter model of express delivery personnel or takeaway personnel, finally labeling the unregistered personnel, and recording capture time and place. And after the steps are cycled for a plurality of times, the space-time law of a large number of unregistered persons who frequently enter and exit the access control system is accumulated.
Taking a real open type city village rental house entrance and exit data import test as an example, the number of the online rental houses is 31, and accumulated effective snapshot data of nearly one month are collected for analysis. According to the statistical analysis of data, when a gridder uses unregistered personnel for filtering, the accuracy can be verified basically to be more than 93% by using 5 times of occurrence of a certain building within 12 days (the accuracy is calculated to be 1 time of occurrence of a plurality of times of occurrence of the certain building on the same day) as a filtering condition, and if a condition with higher frequency is set, although the higher accuracy can be obtained, a certain proportion of omission occurs. Wherein verifying the accuracy means verifying by the grid staff the proportion of people who are actually enrolled but not enrolled who are in the pushed list of people.
Example 2
Referring to fig. 1, after the faces of the fingers in which the comparison is successful or failed are characterized, the similarity degree of the two face images is judged by calculating the feature similarity. In the present invention, the default of successful comparison means that the similarity reaches more than 93.5%, and the comparison is considered to be failed if the similarity is less than the similarity.
And the model filtering in the graph is a filtering model kdModelFilter obtained by inputting a takeout person or a courier into a mask deep learning framework by using big data and performing machine learning training. After the model is trained, the accuracy rate of identifying the takeout personnel or the courier through the work clothes or the work caps can reach more than 85%, and higher identification accuracy rate can be obtained through repeated iterative training of accumulating more data in the later period.
According to the embodiment 1, the webService service is deployed on the cloud server and provides an interface for inquiring unregistered personnel and an interface for registering and managing authority of villages and vulnerabilities in cities.
According to embodiment 1, the grid member can log in through a web browser and authorization information to obtain the unregistered person track of the jurisdiction. According to the personnel track, the time information or the location information of people who are not registered but go in and out of the building for a long time can be accurately positioned, and therefore the grid personnel or the house manager can conveniently carry out door-to-door supplementary registration.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (7)
1. A method of analyzing unregistered but actually living persons based on access control data, comprising: access control system, the sensor, mobile terminal APP, third party database, video acquisition ware and the high in the clouds server that detect the entrance guard state of opening and close, its characterized in that includes following step:
s1: and importing and loading the tenant and the householder information of the personnel who have already entered the house through the mobile terminal APP or the third-party database, wherein the tenant and the householder information of the personnel who have entered the house comprise: comparing the biological characteristics of the certificate photo, the life photo, the video data, the fingerprint and the like;
s2: the sensor for detecting the opening and closing state of the entrance guard detects that people enter and exit the building, and automatically triggers a video collector fixedly installed on an entrance guard system to collect video data or photo data within the time period of the people entering and exiting the building as long as door opening and closing operation occurs;
s3: the cloud server calls video data or photo data collected by the video collector in the time period, the video data or photo data collected by the video collector and the tenant and householder information of the people who live in S1 are compared and identified through a face recognition module of the cloud server, if the video data or photo data are matched, the video data or photo data are classified as matched data, the matched data are returned to S1 for archiving again, the video data or photo data are archived as registered people, and if the video data or photo data are not matched, the video data or the photo data are classified as unmatched data;
s4: marking the unmatched materials in the S3 as unregistered persons, and establishing unregistered person labels according to the unmatched materials; when the steps from S1 to S3 are circulated again, and the matching of the registered person fails, the registered person is matched with the unregistered person, and the video data or the photo data of the same person entering and exiting the access control system are classified under the label of the person according to the contrast similarity;
s5: and looping S1 to S4, accumulating the video data or the photo data of the unregistered personnel, and establishing an unregistered personnel tag library to obtain the space-time rules of all the unregistered personnel.
2. The method for analyzing unregistered but actually living people based on entrance guard data according to claim 1, wherein a face recognition module of the cloud server can perform attribute model extraction on video data or photo data of the unregistered people, perform training recognition on feature data such as work clothes, work caps and signs, and establish label classification storage.
3. The method of claim 1, wherein the access control system and the cloud server are both provided with a remote communication module, and the remote communication module comprises: any one or more of a WIFI module, a 5G communication module, a 4G communication module, a 3G communication module and a 2G communication module.
4. The method for analyzing unregistered but actually checked-in persons based on entrance guard data as claimed in claim 1, wherein the cloud server is configured to process and integrate the three-dimensional face information and mapping information thereof obtained by importing and loading the video collector, the mobile terminal APP and a third-party database, and complete the loop operations of steps S1 to S4.
5. The method for analyzing unregistered but actually living people based on entrance guard data according to claim 1 or 2, wherein the face recognition module is used for recognizing, analyzing and calculating face data collected by the video collector, all information of each user entering and exiting the entrance guard system is synthesized through a face information iterative algorithm, and iterative learning is completed along with continuous updating of the data.
6. The method for analyzing unregistered but actually living in personnel based on entrance guard data according to claim 1, characterized in that the video collector is fixedly installed on an entrance guard machine of the entrance guard system, the video collector is electrically connected with a sensor for detecting the opening and closing states of the entrance guard, when the entrance guard machine is opened, the sensor starts the video collector to capture personnel to enter the whole process of the entrance guard system, when the entrance guard machine is closed, the sensor closes the video collector, and the video collector is in communication connection with the cloud server through the remote communication module.
7. The method of claim 1, wherein the latticer can access the cloud server through a WEB interface, query a tag library of unregistered persons, query data of the unregistered persons within a grid range according to frequency or a building, and further perform business processing.
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Cited By (5)
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| CN111639840A (en) * | 2020-05-14 | 2020-09-08 | 杭州海康威视系统技术有限公司 | Hotel management state monitoring method and device |
| CN112052346A (en) * | 2020-09-11 | 2020-12-08 | 讯飞智元信息科技有限公司 | Method and device for updating real personnel library, electronic equipment and storage medium |
| CN112330514A (en) * | 2020-09-29 | 2021-02-05 | 佳都新太科技股份有限公司 | Community public security information evaluation method, device, equipment and storage medium |
| CN114613044A (en) * | 2022-03-12 | 2022-06-10 | 云知声智能科技股份有限公司 | Pedestrian passageway passing method and device, electronic equipment and storage medium |
| CN114743300A (en) * | 2021-12-09 | 2022-07-12 | 全民认证科技（杭州）有限公司 | Access control method and system based on behavior big data model |
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